Semantically aware keypoint matching

ABSTRACT

A method for keypoint matching includes receiving an input image obtained by a sensor of an agent. The method also includes identifying a set of keypoints of the received image. The method further includes augmenting the descriptor of each of the keypoints with semantic information of the input image. The method also includes identifying a target image based on one or more semantically augmented descriptors of the target image matching one or more semantically augmented descriptors of the input image. The method further includes controlling an action of the agent in response to identifying the target.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims the benefit of U.S. Provisional PatentApplication No. 63/009,930, filed on Apr. 14, 2020, and titled“SEMANTICALLY AWARE SELF-SUPERVISED KEYPOINT LEARNING,” the disclosureof which is expressly incorporated by reference in its entirety.

FIELD OF THE DISCLOSURE

Aspects of the present disclosure generally relate to keypoint matching,and more particularly to techniques and apparatuses for semanticallyaware keypoint matching.

BACKGROUND

Keypoint matching may be used for image registration and localization.For example, a query image may be localized by matching keypoints of thequery image with keypoints of images in a database. Conventional neuralnetworks may be trained to match keypoints. In some examples,conventional systems may detect features and match detected featuresindependent from certain geometric transformations, such as imagetranslation, scale, and rotation. In some such examples, theconventional systems extract keypoints (e.g., characteristic points,feature points, or interest points) and generate a descriptor for eachkeypoint. The descriptor may be a string describing the keypoint. Insuch examples, the descriptor may be independent from geometrictransformation. A machine-vision system may use one or both of thekeypoints or descriptors to identify or track features in one or moreimages.

SUMMARY

In one aspect of the present disclosure, a method for keypoint matchingincludes receiving an input image obtained by a sensor of an agent. Themethod further includes identifying a set of keypoints of the receivedimage. Each of the keypoints may correspond to a different descriptor.The method still further includes augmenting the descriptor of each ofthe keypoints with semantic information of the input image. The methodalso includes identifying a target image based on one or moresemantically augmented descriptors of the target image matching one ormore semantically augmented descriptors of the input image. The methodfurther includes controlling an action of the agent in response toidentifying the target.

Another aspect of the present disclosure is directed to an apparatus forkeypoint matching. The apparatus includes means for receiving an inputimage obtained by a sensor of an agent. The apparatus further includesmeans for identifying a set of keypoints of the received image. Each ofthe keypoints may correspond to a different descriptor. The apparatusstill further includes means for augmenting the descriptor of each ofthe keypoints with semantic information of the input image. Theapparatus also includes means for identifying a target image based onone or more semantically augmented descriptors of the target imagematching one or more semantically augmented descriptors of the inputimage. The apparatus further includes means for controlling an action ofthe agent in response to identifying the target.

In another aspect of the present disclosure, a non-transitorycomputer-readable medium with non-transitory program code recordedthereon for keypoint matching is disclosed. The program code is executedby a processor and includes program code to receive an input imageobtained by a sensor of an agent. The program code further includesprogram code to identify a set of keypoints of the received image. Eachof the keypoints may correspond to a different descriptor. The programcode still further includes program code to augment the descriptor ofeach of the keypoints with semantic information of the input image. Theprogram code also includes program code to identify a target image basedon one or more semantically augmented descriptors of the target imagematching one or more semantically augmented descriptors of the inputimage. The program code further includes program code to control anaction of the agent in response to identifying the target.

Another aspect of the present disclosure is directed to an apparatushaving a memory, one or more processors coupled to the memory, andinstructions stored in the memory and operable, when executed by theprocessor, to cause the apparatus to receive an input image obtained bya sensor of an agent. The execution of the instructions also cause theapparatus to identify a set of keypoints of the received image. Each ofthe keypoints may correspond to a different descriptor. The execution ofthe instructions further cause the apparatus to augment the descriptorof each of the keypoints with semantic information of the input image.The execution of the instructions still further cause the apparatus toidentify a target image based on one or more semantically augmenteddescriptors of the target image matching one or more semanticallyaugmented descriptors of the input image. The execution of theinstructions also cause the apparatus to control an action of the agentin response to identifying the target.

Aspects generally include a method, apparatus, system, computer programproduct, non-transitory computer-readable medium, user equipment, basestation, wireless communications device, and processing system assubstantially described with reference to and as illustrated by theaccompanying drawings and specification.

The foregoing has outlined rather broadly the features and technicaladvantages of examples according to the disclosure in order that thedetailed description that follows may be better understood. Additionalfeatures and advantages will be described. The conception and specificexamples disclosed may be readily utilized as a basis for modifying ordesigning other structures for carrying out the same purposes of thepresent disclosure. Such equivalent constructions do not depart from thescope of the appended claims. Characteristics of the concepts disclosed,both their organization and method of operation, together withassociated advantages will be better understood from the followingdescription when considered in connection with the accompanying figures.Each of the figures is provided for the purposes of illustration anddescription, and not as a definition of the limits of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 illustrates an example of a vehicle in an environment accordingto aspects of the present disclosure.

FIG. 2 illustrates an example of a training scheme according to aspectsof the present disclosure.

FIG. 3 is a diagram illustrating an example of a hardware implementationaccording to aspects of the present disclosure.

FIG. 4 illustrates a flow diagram for a method according to aspects ofthe present disclosure.

FIG. 5 is a diagram illustrating an example of a query image and acorresponding target image retrieved as a matching image associated withthe query image, in accordance with aspects of the present disclosure.

FIG. 6 is a diagram illustrating an example of a hardware implementationfor a semantically aware keypoint matching system, according to aspectsof the present disclosure.

FIG. 7 is a diagram illustrating an example process performed, forexample, with a semantically aware keypoint matching model, inaccordance with various aspects of the present disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for the purpose of providing athorough understanding of the various concepts. It will be apparent tothose skilled in the art, however, that these concepts may be practicedwithout these specific details. In some instances, well-known structuresand components are shown in block diagram form in order to avoidobscuring such concepts.

An agent, such as an autonomous agent, may reconstruct athree-dimensional map of a scene based on one or more images obtainedfrom a sensor. The agent may also localize its location in anenvironment (e.g., a map of the environment) based on sensor information(e.g., GPS information). Localization and scene reconstruction may beused to perform various tasks, such as scene understanding, motionplanning, and/or obstacle avoidance. For example, the agent mayautonomously navigate through an environment based on the localizationinformation and the scene reconstruction. Depth estimation may be usedto understand (e.g., reconstruct) the structure of a scene.

Conventional machine-vision based systems, such as autonomous vehicles,may use a LIDAR sensor to build a 3D spatial representation of theworld. The 3D spatial representation is localized against a pre-built 3Dmap. In such conventional systems, keypoint matching may be specifiedfor localizing the 3D representation against the pre-built 3D map toreconstruct a three-dimensional map of a scene based on one or moreimages, such as 3D images, obtained from a sensor, such as a LIDARsensor. That is, conventional keypoint matching systems match 3Dkeypoints obtained from a LIDAR sensor to 3D keypoints of the pre-builtmap. Due to costs and accuracy, it may be desirable to match 2Dkeypoints to 3D keypoints of the pre-built map.

Specifically, LIDAR sensors used for 3D keypoint matching may be costly,bulky, and resource-intensive. Additionally, the LIDAR sensors' accuracymay be reduced in some environments, such as, for example, rain, fog,wet surfaces, non-retroreflective road markings, etc. In contrast, acamera, such as a red-green-blue (RGB) camera, may provide both a densesemantic and spatial understanding of the scene by reasoning acrossspace (stereo, multi-camera) and time (multi-view reconstruction).Additionally, a camera may be less costly, smaller, and lessresource-intensive in comparison to LIDAR. Therefore, it may bedesirable to use one or more cameras for a machine-vision systems. Suchcameras may be used for 2D keypoint matching.

Aspects of the present disclosure improve keypoint matching systems tomatch a 2D keypoint obtained from an image, such as a monocular image,with a 3D keypoint of a pre-built map. In some implementations, theLIDAR sensor may be replaced by a camera. In such implementations, amachine-vision system may be trained to match distinctly illuminatedkeypoints and viewpoint invariant keypoints to improve a localizationfunction and a mapping function. Such machine-vision systems maygenerate consistent 3D maps by reliably matching and triangulatingkeypoints between frames in a monocular sequence. The matched keypointsmay be localized against the pre-built map by using a 2D-3D matchingmethod. In some examples, a created map may be semantically richer thana LIDAR-based map because an image, such as a monocular image, issemantically richer than an output of a LIDAR sensor. In such examples,the semantically richer map may improve one or more tasks, such asdetecting map changes and online camera calibration. Some aspects of thepresent disclosure are also directed to improving a training process forthe keypoint matching system.

FIG. 1 illustrates an example of an ego vehicle 100 (e.g., ego agent) inan environment 150 according to aspects of the present disclosure. Asshown in FIG. 1, the ego vehicle 100 is traveling on a road 110. A firstvehicle 104 (e.g., other agent) may be ahead of the ego vehicle 100, anda second vehicle 116 may be adjacent to the ego vehicle 100. In thisexample, the ego vehicle 100 may include a 2D camera 108, such as a 2DRGB camera, and a second sensor 106. The second sensor 106 may beanother RGB camera or another type of sensor, such as RADAR and/orultrasound. Additionally, or alternatively, the ego vehicle 100 mayinclude one or more additional sensors. For example, the additionalsensors may be side facing and/or rear facing sensors.

In one configuration, the 2D camera 108 captures a 2D image thatincludes objects in the 2D camera's 108 field of view 114. The secondsensor 106 may generate one or more output streams. The 2D imagecaptured by the 2D camera includes a 2D image of the first vehicle 104,as the first vehicle 104 is in the 2D camera's 108 field of view 114.

The information obtained from the sensors 106, 108 may be used tonavigate the ego vehicle 100 along a route when the ego vehicle 100 isin an autonomous mode. The sensors 106, 108 may be powered fromelectricity provided from the vehicle's 100 battery (not shown). Thebattery may also power the vehicle's motor. The information obtainedfrom the sensors 106, 108 may be used for keypoint matching.

As described, keypoint matching may be specified for one or both ofimage registration or localization. For example, keypoints may bematched between a query image and a database of images to localize thequery image in the database. A keypoint matching system may be trainedbased on labeled data. Such training may be difficult due to a lack ofconsistency of training samples for interest points in natural images.Specifically, interest points are difficult to clearly and consistentlydefine for a human annotator.

Some conventional systems train a keypoint estimation model usinghomography adaptation with non-spatial image augmentations to create 2Dsynthetic views. The homography adaptation trained models may fail toaccount for illumination changes and 3D non-planar scenes. Some otherconventional systems may use a Structure-from-Motion (SfM) function togenerate training samples under different viewpoints and lightingconditions. Such models are trained on small patches rather than wholeimages, thereby restricting an area from which the model may learndescriptors. Additionally, training time may increase based on the useof small patches. In still some other conventional systems, anoff-the-shelf SfM function obtains respective depth maps as well ascamera intrinsics and extrinsics of a training sequence. In suchsystems, ground truth keypoint correspondences between consecutiveframes are determined from the camera intrinsics and extrinsics. Modelstrained with the off-the-shelf SfM function may not share computationsbetween the detector and descriptor, thereby increasing resource use andreducing efficiency. Such models trained with the off-the-shelf SfMfunction may also use patches, which restrict the area from which thenetwork can learn descriptors.

Still, some other conventional systems may first estimate 2D keypointsand descriptors for each image in a monocular sequence. A model of suchsystem may then use a bundle adjustment method to classify a stabilityof the estimated 2D keypoints based on re-projection error. Thestability may be used as a supervision signal to re-train the model.This model is not fully differentiable, thus, the model cannot betrained in an end-to-end manner. Additionally, the model is notsemantically aware.

As described, conventional training methods may limit the performance ofa keypoint matching system. Aspects of the present disclosure aredirected to improving keypoint matching systems by augmenting a keypointdescriptor with semantic information. In some implementations, thedescriptor may be augmented by embedding semantic information fromanother pre-trained segmentation network.

For machine-vision systems, as well as other types of systems, semanticsegmentation accounts for semantic information of a scene and ignoresthe instance relationship between pixels with a same semantic label. Insome examples, two different cars may receive the same label (e.g., car)in a semantic segmentation map. In contrast, instance segmentationidentifies individual instances of the same object and may not identifyuncountable objects (e.g., background objects).

A semantically segmented map may include labels for individual pixels ofan input, such as a red-green-blue image. For example, in an image of aroad, individual pixels may be associated with a label selected from oneor more predetermined labels (e.g., semantic labels), such as car, road,background, sign, or other objects found in a road image. In thisexample, the agent may use semantic segmentation to distinguish the roadfrom other objects, such that the agent may safely move within anenvironment.

FIG. 2 is a diagram illustrating an example of a semantically segmentedmap, in accordance with aspects of the present disclosure. In theexample of FIG. 2, an agent, such as an autonomous vehicle or an egovehicle 100 as described with reference to FIG. 1, may capture an imagevia a sensor, such as a monocular camera. In the example of FIG. 2, theimage may be semantically segmented via a trained semantic segmentationmodel, such as an panoptic segmentation network, to generate thesemantically segmented image 200. The panoptic segmentation network maybe an example of an artificial neural network. The trained model may beincorporated with one or more systems of the agent. Additionally, oralternatively, the trained model may be stored in a remote system andthe agent may wirelessly transmit the unlabeled image to receive thesemantically segmented image 200.

In the example of FIG. 2, each pixel of the semantically segmented image200 is associated with a semantic label. In the current example, thetrained model labels pixels corresponding to pedestrians 202, cars 204,a train 206, sidewalks 208, street signs 210, vegetation 212, buildings214 (e.g., structures), and crosswalks 216. Aspects of the presentdisclosure are not limited to labeling the discussed elements, otherelements, such as poles for holding street signs may be labeled. Forsimplicity, some labels are omitted. Additionally, aspects of thepresent disclosure are not limited to labeling every pixel. In someexamples, labels may be omitted from one or more pixels.

In some implementations, a descriptor of a keypoint model may beaugmented with semantic information obtained from a trained semanticsegmentation model, such as a panoptic segmentation network. FIG. 3illustrates an example of a keypoint model 300, in accordance withaspects of the present disclosure. In some implementations, as shown inFIG. 3, the keypoint model 300 receives an input image 302 and outputskeypoint scores 306, keypoint locations 308, and keypoint descriptors310 corresponding to the input image 302. In some examples, the inputimage 302 may be a 2D image captured by a camera, such as a monocularcamera, integrated with an agent, such as an autonomous agent or an egovehicle 100 as described with reference to FIG. 1.

A total number of keypoints in the input image 302 may vary based on aresolution of the input image. In some implementations, the keypointmodel 300 is a component of the framework for matching a source image,such as the input image 302, to a target image. The keypoint model 300may be trained to match the source image to the target image in aself-supervised manner. The source image and the target image may berelated through a known homography transformation, which warps a pixelfrom the source image and maps it into the target image.

The keypoint model 300 may be an encoder-decoder style network. Theencoder may include a number of VGG-style blocks, such as four VGG-styleblocks, stacked to reduce the resolution (H×W) of the image 302. In someexamples, the resolution is reduced to H/8×W/8. The reduced resolutionmay improve keypoint location predictions and descriptor predictions. Inthis low resolution embedding space, each pixel may correspond to a cellin the input image 302. In some examples, the cell is an 8×8 cell. Thedecoder may include three separate heads for the keypoints (e.g.,locations), descriptors, and scores, respectively.

In some implementations, as shown in FIG. 3, for each pixel of the image302 processed by the keypoint model 300, the keypoint model 300 outputsa keypoint location 308 relative to the grid, such as the 8×8 grid,corresponding to the respective pixel. For each pixel, the keypointlocation 308 may be coordinates of the input image accounting for thegrid's position in an embedding of the encoder of the keypoint model300. In some such implementations, a corresponding keypoint location maybe determined in a target image after warping via the known homography.For each warped keypoint, a closest corresponding keypoint in the targetimage may be associated with the keypoint of the source image 302 basedon Euclidean distance. In some examples, keypoint pairs may be discardedwhen a distance between keypoints satisfies a discard condition, such asthe distance being greater than a distance threshold.

In the example of FIG. 3, for each pixel of the image 302 processed bythe keypoint model 300, the keypoint model 300 also outputs a keypointdescriptor 310. Each keypoint descriptor 310 may be a string determinedbased on features of the image 302 obtained from the encoding process ofthe keypoint model 300. In some implementations, the encoded image maybe upsampled prior to determining each keypoint descriptor 310, suchthat each keypoint descriptor 310 captures a greater amount of details.The descriptor of each keypoint in the input image 302 may be obtainedby sampling an appropriate location in a dense descriptor map. Theassociated descriptor in a target frame may be obtained by sampling theappropriate location in the target descriptor map based on the warpedkeypoint position.

Additionally, in some implementations, as shown in FIG. 3, the keypointmodel 300 may generate a keypoint score 312 associated with eachkeypoint descriptor 310, respectively. At test time, reliable keypointsmay be identified based on an associated keypoint score 312. A subset ofthe reliable keypoints may be selected for keypoint matching. In someexamples, a reliable keypoint may be a keypoint associated with akeypoint score 312 that satisfies a reliability condition, such ashaving a keypoint score 312 that is greater than a threshold, or akeypoint score 312 within a top percentage of all keypoint scores 312.In some such examples, the reliable keypoints may be selected formatching, such that feature pairs may have consistent keypoint scores.Additionally, during training, the keypoint model 300 may learn todistinguish desirable keypoints based on the keypoint scores 312.

In some implementations, each keypoint descriptor 310 may be augmentedwith semantic information of a corresponding keypoint location. FIG. 4is a block diagram illustrating an example of a semantically awarekeypoint matching model 400, in accordance with aspects of the presentdisclosure. In the example of FIG. 4, an input image 302 is input to akeypoint model 300 to generate keypoint scores 306, keypoint locations308, and keypoint descriptors 310 corresponding to the input image 302,as described with reference to FIG. 3. In some examples, the input image302 may be referred to as a query image.

In some implementations, as shown in FIG. 4, the input image 302 mayalso be input to a semantic segmentation model 402. In the example ofFIG. 4, the semantic segmentation model 402 is a component of thesemantically aware keypoint matching model 400. Alternatively, thesemantic segmentation model 402 may be a separate component working inconjunction with the semantically aware keypoint matching model 400. Insome examples, the semantic segmentation model 402 may be a pre-trainedpanoptic segmentation network having an encoder-decoder architecture.The semantic segmentation model 402 may generate a semanticallysegmented image, such as the semantically segmented image 200 describedwith reference to FIG. 2. In some implementations, features of asemantic feature map may be output from decoders of the semanticsegmentation model 402 and augmented with one or more layers, such asdecoders or a final encoder output, of the keypoint model 300. That is,the semantic information of the input image 302 may be distilled withthe keypoint descriptors 310 generated for the input image 302. As such,a pixel of the input image 302 may be associated with a keypointdescriptor 310 that is augmented with semantic information, such assemantic features and/or a semantic label.

In some implementations, the semantic features of the input image 302may be embedded into a target space, such that nearest neighbor matching(e.g., using L1 or L2 distance) may be applied to find a matching pairof keypoints between frames, such as the input image 302 and the targetimage 404. In such implementations, the keypoint descriptors 310 aresemantically aware. That is, the keypoint descriptors 310 include scenelevel information which improves the ability of the semantically awarekeypoint matching model 400 to match keypoints between frames, such asthe input image 302 and the target image 404. In some implementations,the semantic features are jointly optimized with local information(e.g., the keypoint descriptors 310). In such implementations, thekeypoint matching may be more robust to different angles and/or lightingin comparison to conventional keypoint matching networks.

In the example of FIG. 4, the semantic segmentation model 400 may querya database 406 (e.g., a storage device) with the semantically awaredescriptors to identify the target image 404. In some implementations,an image retrieval system associated with the database 406 may determineone or more matching image(s) by identifying one or more images of themultiple images stored in the database 406 matching the keypointdescriptors 310. The one or more matching images may be retrieved as thetarget image(s) 404 and provided as an output to the semantically awarekeypoint matching model 400. Additionally, or alternatively, the targetimage 404 may be output to a component associated with the semanticallyaware keypoint matching model 400, such as a navigation component. Insome implementations, the semantically aware keypoint matching model 400may be a component of the image retrieval system (not shown in FIG. 4).

In some implementations, a final output, or the output of anintermediate layer, of the semantic segmentation model 402 may befurther processed via an additional adaptation convolutional layerbefore embedding into a descriptor feature map corresponding to thekeypoint descriptor 310.

In some implementations, the input image 302 may be one frame of asequence of frames captured by a sensor, such as a monocular camera. Thesequence of frames may be stored in a memory associated with thesemantic segmentation model 402. The memory and the semanticsegmentation model 402 may be components of a device or system of anagent, such as an autonomous vehicle. Aspects of the present disclosureare not limited to processing each frame of the sequence of frames. Insome aspects, a subset of the sequence of frames may be processed forkeypoint matching based on a desired task and/or a desired level ofaccuracy for the task, such as a localization task.

In some implementations, the semantically aware keypoint matching model400 segments each input image 302 (e.g., each frame of a sequence offrames) into multiple regions. In such implementations, the semanticallyaware keypoint matching model 400 may also determine a set of keypointsfor each input image 302. In some such implementations, the semanticallyaware keypoint matching model 400 selects a set of keypoints withkeypoint scores 306 that satisfy selection criteria, such as thekeypoint score of a keypoint being less than a threshold. In someexamples, a keypoint may be an example of a reference element in theinput image 302.

In some implementations, the semantically aware keypoint matching model400 tracks one or more regions of the input image 302 (e.g., sourceimage) to one or more regions of a target image, resulting in a matchedregion in the target image. The target image may be an image of alibrary of images stored in the memory associated with the semanticallyaware keypoint matching model 400. Each image of the library of imagesmay include keypoints and corresponding descriptors. In someimplementations, each descriptor is augmented with semantic segmentationinformation (e.g., semantic segmentation features).

In some implementations, the set of keypoints may be robust keypoints.Such keypoints may be repeated across multiple frames. The keypoints maybe obtained using different techniques. In some examples, the keypointmodel 300 may be trained in a self-supervised manner to detect keypointsof an input image 302. As described, in some implementations, theselected keypoints may be matched between the input image 302 and thetarget image 404. In some implementations, the keypoint matchingconsiders both a similarity of keypoint descriptors 310 associated withkeypoints of the input image 302 and the target image 404, as well as asimilarity of semantic information associated with keypoints of theinput image 302 and the target image 404. In some such implementations,the keypoint matching may be biased toward keypoints associated with thesame semantic label.

As described, a keypoint descriptor 310 may provide appearanceinformation of an associated keypoint. In some examples, for a keypoint,the associated keypoint descriptor 310 may provide informationidentifying one or more of the keypoint's color, average color, colorhistogram, shape, compactness, eccentricity, or texture (e.g.,Gabor-based descriptor). Other types of information may be provided byeach keypoint descriptor 310. The keypoints of the input image 302 andtarget image 404 may be matched based on a Euclidean distance in thedescriptor space, while also considering the semantic information.Additionally, or alternatively, the keypoints of the input image 302 andtarget image 404 may be matched based on nearest neighbor matching.

FIG. 5 is a diagram illustrating an example of a query image 500 and acorresponding target image 502 retrieved as a matching image associatedwith the query image 500. As described, the target image 502 may beretrieved from a storage system, such as a memory device of the agent ora cloud-based storage system based on the semantically augmenteddescriptors of the query image 500 matching the semantically augmenteddescriptors of the target image 502. Feature correspondences between thequery image 500 and the target image 502 are depicted using connectorlines 504 between corresponding features. Connector lines 504 mayconnect a center of receptive fields for matching features. Theconnector lines 504 are provided for illustrative purposes. A number ofconnector lines 504 may correspond to a number of matching keypoints. Insome examples, during testing (e.g., real-world deployment), theconnector lines 504 may not be generated between matched images 500 and502.

FIG. 6 is a diagram illustrating an example of a hardware implementationfor a semantically aware keypoint matching system 600, according toaspects of the present disclosure. The depth-aware semantically awarekeypoint matching system 600 may be a component of a vehicle, a roboticdevice, or another device. For example, as shown in FIG. 4, thesemantically aware keypoint matching system 600 is a component of avehicle 628. The vehicle 628 may be an example of the ego vehicle 100described with reference to FIG. 1. Aspects of the present disclosureare not limited to the semantically aware keypoint matching system 600being a component of the vehicle 628, as other types of agents, such asa bus, boat, drone, or robot, are also contemplated for using theocclusion information prioritization system 600.

The vehicle 628 may operate in one or more of an autonomous operatingmode, a semi-autonomous operating mode, and a manual operating mode.Furthermore, the vehicle 628 may be an electric vehicle, a hybridvehicle, a fuel vehicle, or another type of vehicle.

The depth-aware semantically aware keypoint matching system 600 may beimplemented with a bus architecture, represented generally by a bus 660.The bus 660 may include any number of interconnecting buses and bridgesdepending on the specific application of the semantically aware keypointmatching system 600 and the overall design constraints. The bus 660links together various circuits, including one or more processors and/orhardware modules, represented by a processor 620, a communication module622, a location module 618, a sensor module 602, a locomotion module626, a navigation module 624, and a computer-readable medium 614. Thebus 660 may also link various other circuits such as timing sources,peripherals, voltage regulators, and power management circuits, whichare well known in the art, and therefore, will not be described anyfurther.

The semantically aware keypoint matching system 600 includes atransceiver 616 coupled to the processor 620, the sensor module 602, akeypoint module 608, the communication module 622, the location module618, the locomotion module 626, the navigation module 624, and thecomputer-readable medium 614. The transceiver 616 is coupled to anantenna 644.

The semantically aware keypoint matching system 600 includes theprocessor 620 coupled to the computer-readable medium 614. The processor620 performs processing, including the execution of instructions storedon the computer-readable medium 614 providing functionality according tothe disclosure. The instructions, when executed by the processor 620,cause the semantically aware keypoint matching system 600 to perform thevarious functions described for a particular device, such as the vehicle628, or any of the modules 602, 608, 614, 616, 618, 620, 622, 624, 626.The computer-readable medium 614 may also be used for storing data thatis manipulated by the processor 620 when executing the instructions.

The sensor module 602 may be used to obtain measurements via differentsensors, such as a first sensor 606 and a second sensor 604. The firstsensor 606 may be a vision sensor, such as a stereoscopic camera or ared-green-blue (RGB) camera, for capturing 2D images. The second sensor604 may be a ranging sensor, such as a light detection and ranging(LIDAR) sensor or a radio detection and ranging (RADAR) sensor. Ofcourse, aspects of the present disclosure are not limited to theaforementioned sensors as other types of sensors, such as, for example,thermal, sonar, and/or lasers are also contemplated for either of thesensors 604, 606.

The measurements of the first sensor 606 and the second sensor 604 maybe processed by one or more of the processor 620, the sensor module 602,the keypoint module 608, the communication module 622, the locationmodule 618, the locomotion module 626, the navigation module 624, inconjunction with the computer-readable medium 614 to implement thefunctionality described herein. In one configuration, the data capturedby the first sensor 606 and the second sensor 604 may be transmitted toan external device via the transceiver 616. The first sensor 606 and thesecond sensor 604 may be coupled to the vehicle 628 or may be incommunication with the vehicle 628.

The location module 618 may be used to determine a location of thevehicle 628. For example, the location module 618 may use a globalpositioning system (GPS) to determine the location of the vehicle 628.The communication module 622 may be used to facilitate communicationsvia the transceiver 616. For example, the communication module 622 maybe configured to provide communication capabilities via differentwireless protocols, such as WiFi, long term evolution (LTE), 4G, newradio (NR) (e.g., 5G), etc. The communication module 622 may also beused to communicate with other components of the vehicle 628 that arenot modules of the occlusion information prioritization system 600.

The locomotion module 626 may be used to facilitate locomotion of thevehicle 628. As an example, the locomotion module 626 may control amovement of the wheels. As another example, the locomotion module 626may be in communication with one or more power sources of the vehicle628, such as a motor and/or batteries. Of course, aspects of the presentdisclosure are not limited to providing locomotion via wheels and arecontemplated for other types of components for providing locomotion,such as propellers, treads, fins, and/or jet engines.

The semantically aware keypoint matching system 600 also includes thenavigation module 624 for planning a route or controlling the locomotionof the vehicle 628, via the locomotion module 626. The various modulesin FIG. 6 may be software modules running in the processor 620,resident/stored in the computer-readable medium 614, one or morehardware modules coupled to the processor 620, or some combinationthereof.

The keypoint module 608 may identify keypoints for image registrationand/or localization, the keypoint module 608 may be an example of thesemantically aware keypoint matching model 400 as described in referenceto FIG. 4. The keypoint module 608 may include a memory 680. The memory680 may be integrated with the keypoint module 608 or may be a componentof the image localization system 600. The memory 680 may includevolatile and/or non-volatile memory. For example, the memory 680 may beread only memory (ROM), programmable ROM (PROM), electronic programmableROM (EPROM), electronic erasable PROM (EEPROM), flash memory, randomaccess memory (RAM), or other types of volatile or non-volatile memory.Additionally, the RAM may be, for example, synchronous RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), sync link DRAM, (SLDRAM), directRAM bus RAM (DRRAM), or other types of RAM.

The keypoint module 608 may work in conjunction with one or more of thememory 680, the processor 620, the communication module 622, thelocation module 618, the sensor module 602, the locomotion module 626,the navigation module 624, and the computer-readable medium 614 toperform one or more functions described below. In some examples, thekeypoint module 608 receives an input image obtained by a sensor of anagent. Additionally, the keypoint module 608 may identify a set ofkeypoints of the received image, each of the keypoints corresponding toa different descriptor. In such examples, the keypoint module 608 mayalso augment the descriptor of each of the keypoints with semanticinformation of the input image. Furthermore, the keypoint module 608 mayidentify a target image based on one or more semantically augmenteddescriptors of the target image matching one or more semanticallyaugmented descriptors of the input image. Finally, the keypoint module608 may control an action of the agent in response to identifying thetarget. The keypoint module 608 is not limited to performing thedescribed functions, other functions are contemplated.

The National Highway Traffic Safety Administration (NHTSA) has defineddifferent “levels” of autonomous vehicles (e.g., Level 0, Level 1, Level2, Level 3, Level 4, and Level 5). For example, if an autonomous vehiclehas a higher level number than another autonomous vehicle (e.g., Level 3is a higher level number than Levels 2 or 1), then the autonomousvehicle with a higher level number offers a greater combination andquantity of autonomous features relative to the vehicle with the lowerlevel number. These different levels of autonomous vehicles aredescribed briefly below.

Level 0: In a Level 0 vehicle, the set of advanced driver assistancesystem (ADAS) features installed in a vehicle provide no vehiclecontrol, but may issue warnings to the driver of the vehicle. A vehiclewhich is Level 0 is not an autonomous or semi-autonomous vehicle.

Level 1: In a Level 1 vehicle, the driver is ready to take drivingcontrol of the autonomous vehicle at any time. The set of ADAS featuresinstalled in the autonomous vehicle may provide autonomous features suchas: adaptive cruise control (ACC); parking assistance with automatedsteering; and lane keeping assistance (LKA) type II, in any combination.

Level 2: In a Level 2 vehicle, the driver is obliged to detect objectsand events in the roadway environment and respond if the set of ADASfeatures installed in the autonomous vehicle fail to respond properly(based on the driver's subjective judgement). The set of ADAS featuresinstalled in the autonomous vehicle may include accelerating, braking,and steering. In a Level 2 vehicle, the set of ADAS features installedin the autonomous vehicle can deactivate immediately upon takeover bythe driver.

Level 3: In a Level 3 ADAS vehicle, within known, limited environments(such as freeways), the driver can safely turn their attention away fromdriving tasks, but must still be prepared to take control of theautonomous vehicle when needed.

Level 4: In a Level 4 vehicle, the set of ADAS features installed in theautonomous vehicle can control the autonomous vehicle in all but a fewenvironments, such as severe weather. The driver of the Level 4 vehicleenables the automated system (which is comprised of the set of ADASfeatures installed in the vehicle) only when it is safe to do so. Whenthe automated Level 4 vehicle is enabled, driver attention is notrequired for the autonomous vehicle to operate safely and consistentwithin accepted norms.

Level 5: In a Level 5 vehicle, other than setting the destination andstarting the system, no human intervention is involved. The automatedsystem can drive to any location where it is legal to drive and make itsown decision (which may vary based on the jurisdiction where the vehicleis located).

A highly autonomous vehicle (HAV) is an autonomous vehicle that is Level3 or higher. Accordingly, in some configurations the vehicle 628 may beone of a Level 0 non-autonomous vehicle, a Level 1 autonomous vehicle, aLevel 2 autonomous vehicle, a Level 3 autonomous vehicle, a Level 4autonomous vehicle, or a Level 5 autonomous vehicle.

FIG. 7 is a diagram illustrating an example process 700 performed, forexample, with a semantically aware keypoint matching model, inaccordance with various aspects of the present disclosure. The exampleprocess 700 is an example of using keypoint descriptors augmented withsemantic information to match a query image with a target image. In someimplementations, the process 700 may be performed by a semanticallyaware keypoint matching model, such as the semantically aware keypointmatching model 400 described above with reference to FIG. 4. Thesemantically aware keypoint matching model may be a component of anagent, such as the ego vehicle 100 or the vehicle 628 described abovewith reference to FIGS. 1 and 6, respectively.

In some implementations, the process 700 begins in block 702 withreceiving an input image obtained by a sensor of an agent. In block 704,the process 700 identifies a set of keypoints of the received image.Each of the keypoints may correspond to a different descriptor. In block706, the process 700 augments the descriptor of each of the keypointswith semantic information of the input image. Furthermore, in block 708,the process 700 identifies a target image based on one or moresemantically augmented descriptors of the target image matching one ormore semantically augmented descriptors of the input image. Finally, inblock 710, the process 700 controls an action of the agent in responseto identifying the target

Based on the teachings, one skilled in the art should appreciate thatthe scope of the present disclosure is intended to cover any aspect ofthe present disclosure, whether implemented independently of or combinedwith any other aspect of the present disclosure. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth. In addition, the scope of the presentdisclosure is intended to cover such an apparatus or method practicedusing other structure, functionality, or structure and functionality inaddition to, or other than the various aspects of the present disclosureset forth. It should be understood that any aspect of the presentdisclosure may be embodied by one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the presentdisclosure. Although some benefits and advantages of the preferredaspects are mentioned, the scope of the present disclosure is notintended to be limited to particular benefits, uses or objectives.Rather, aspects of the present disclosure are intended to be broadlyapplicable to different technologies, system configurations, networksand protocols, some of which are illustrated by way of example in thefigures and in the following description of the preferred aspects. Thedetailed description and drawings are merely illustrative of the presentdisclosure rather than limiting, the scope of the present disclosurebeing defined by the appended claims and equivalents thereof.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory) and the like.Furthermore, “determining” may include resolving, selecting, choosing,establishing, and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a processor specially configured to perform the functionsdiscussed in the present disclosure. The processor may be a neuralnetwork processor, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate arraysignal (FPGA) or other programmable logic device (PLD), discrete gate ortransistor logic, discrete hardware components or any combinationthereof designed to perform the functions described herein.Alternatively, the processing system may comprise one or moreneuromorphic processors for implementing the neuron models and models ofneural systems described herein. The processor may be a microprocessor,controller, microcontroller, or state machine specially configured asdescribed herein. A processor may also be implemented as a combinationof computing devices, e.g., a combination of a DSP and a microprocessor,a plurality of microprocessors, one or more microprocessors inconjunction with a DSP core, or such other special configuration, asdescribed herein.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in storage or machine readable medium,including random access memory (RAM), read only memory (ROM), flashmemory, erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), registers, a hard disk,a removable disk, a CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to carry or store desired program code in the form ofinstructions or data structures and that can be accessed by a computer.A software module may comprise a single instruction, or manyinstructions, and may be distributed over several different codesegments, among different programs, and across multiple storage media. Astorage medium may be coupled to a processor such that the processor canread information from, and write information to, the storage medium. Inthe alternative, the storage medium may be integral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may be used to connect a networkadapter, among other things, to the processing system via the bus. Thenetwork adapter may be used to implement signal processing functions.For certain aspects, a user interface (e.g., keypad, display, mouse,joystick, etc.) may also be connected to the bus. The bus may also linkvarious other circuits such as timing sources, peripherals, voltageregulators, power management circuits, and the like, which are wellknown in the art, and therefore, will not be described any further.

The processor may be responsible for managing the bus and processing,including the execution of software stored on the machine-readablemedia. Software shall be construed to mean instructions, data, or anycombination thereof, whether referred to as software, firmware,middleware, microcode, hardware description language, or otherwise.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all which may be accessed by the processor through thebus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or specialized register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The machine-readable media may comprise a number of software modules.The software modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RAM from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a specialpurpose register file for execution by the processor. When referring tothe functionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module. Furthermore, it should beappreciated that aspects of the present disclosure result inimprovements to the functioning of the processor, computer, machine, orother system implementing such aspects.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a computer-readable medium.Computer-readable media include both computer storage media andcommunication media including any storage medium that facilitatestransfer of a computer program from one place to another.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means, such that a user terminal and/or basestation can obtain the various methods upon coupling or providing thestorage means to the device. Moreover, any other suitable technique forproviding the methods and techniques described herein to a device can beutilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes, and variations may be made in the arrangement, operation, anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

What is claimed is:
 1. A method for keypoint matching performed by asemantically aware keypoint matching model, the method comprising:receiving an input image obtained by a sensor of an agent; identifying aset of keypoints of the received image, each of the keypointscorresponding to a different descriptor; augmenting the descriptor ofeach of the keypoints with semantic information of the input image;identifying a target image based on one or more semantically augmenteddescriptors of the target image matching one or more semanticallyaugmented descriptors of the input image; and controlling an action ofthe agent in response to identifying the target.
 2. The method of claim1, further comprising identifying the one or more semantically augmenteddescriptors of the target image matching one or more semanticallyaugmented descriptors of the input image based on a nearest neighbormatching function.
 3. The method of claim 2, further comprising biasingthe matching of the one or more semantically augmented descriptors ofthe target image and the one or more semantically augmented descriptorsof the input image toward descriptors with matching semantic labels. 4.The method of claim 1, further comprising obtaining the semanticinformation from a semantic segmentation model trained to label one ormore pixels of the input image.
 5. The method of claim 1, furthercomprising identifying a current location of the agent based on theidentified target image, wherein controlling the action of the agentcomprises navigating to a location based on the identified currentlocation.
 6. The method of claim 1, further comprising identifying thetarget image from a plurality of stored images, each of the storedimages associated with one or more semantically augmented descriptors.7. The method of claim 1, wherein the agent is an autonomous vehicle. 8.The method of claim 1, further comprising embedding the semanticinformation into a target space; and identifying a matching descriptorbased on nearest neighbor matching.
 9. An apparatus for keypointmatching performed at a semantically aware keypoint matching model, theapparatus comprising: a processor; a memory coupled with the processor;and instructions stored in the memory and operable, when executed by theprocessor, to cause the apparatus: to receive an input image obtained bya sensor of an agent; to identify a set of keypoints of the receivedimage, each of the keypoints corresponding to a different descriptor; toaugment the descriptor of each of the keypoints with semanticinformation of the input image; to identify a target image based on oneor more semantically augmented descriptors of the target image matchingone or more semantically augmented descriptors of the input image; andto control an action of the agent in response to identifying the target.10. The apparatus of claim 9, wherein execution of the instructionsfurther cause the apparatus to identify the one or more semanticallyaugmented descriptors of the target image matching one or moresemantically augmented descriptors of the input image based on a nearestneighbor matching function.
 11. The apparatus of claim 10, whereinexecution of the instructions further cause the apparatus to bias thematching of the one or more semantically augmented descriptors of thetarget image and the one or more semantically augmented descriptors ofthe input image toward descriptors with matching semantic labels. 12.The apparatus of claim 9, wherein execution of the instructions furthercause the apparatus to obtain the semantic information from a semanticsegmentation model trained to label one or more pixels of the inputimage.
 13. The apparatus of claim 9, wherein execution of theinstructions further cause the apparatus to identify a current locationof the agent based on the identified target image, wherein controllingthe action of the agent comprises navigating to a location based on theidentified current location.
 14. The apparatus of claim 9, whereinexecution of the instructions further cause the apparatus to identifythe target image from a plurality of stored images, each of the storedimages associated with one or more semantically augmented descriptors.15. The apparatus of claim 9, wherein the agent is an autonomousvehicle.
 16. The apparatus of claim 9, wherein execution of theinstructions further cause the apparatus to embed the semanticinformation into a target space.
 17. A non-transitory computer-readablemedium having program code recorded thereon for keypoint matchingperformed at a semantically aware keypoint matching model, the programcode executed by a processor and comprising: program code to receive aninput image obtained by a sensor of an agent; program code to identify aset of keypoints of the received image, each of the keypointscorresponding to a different descriptor; program code to augment thedescriptor of each of the keypoints with semantic information of theinput image; program code to identify a target image based on one ormore semantically augmented descriptors of the target image matching oneor more semantically augmented descriptors of the input image; andprogram code to control an action of the agent in response toidentifying the target.
 18. The non-transitory computer-readable mediumof claim 17, wherein the program code further comprises program code toidentify the one or more semantically augmented descriptors of thetarget image matching one or more semantically augmented descriptors ofthe input image based on a nearest neighbor matching function.
 19. Thenon-transitory computer-readable medium of claim 18, wherein the programcode further comprises program code to bias the matching of the one ormore semantically augmented descriptors of the target image and the oneor more semantically augmented descriptors of the input image towarddescriptors with matching semantic labels.
 20. The non-transitorycomputer-readable medium of claim 17, wherein the program code furthercomprises program code to obtain the semantic information from asemantic segmentation model trained to label one or more pixels of theinput image.